{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import h5py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Establish directories\n",
    "data_dir = '/home/jovyan/data/Outputs_noCoverage/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/jovyan/data/Outputs_noCoverage/processed_ATL06_20181110092841_06530106_001_01.h5',\n",
       " '/home/jovyan/data/Outputs_noCoverage/processed_ATL06_20181115210428_07370102_001_01.h5',\n",
       " '/home/jovyan/data/Outputs_noCoverage/processed_ATL06_20181213075606_11560106_001_01.h5',\n",
       " '/home/jovyan/data/Outputs_noCoverage/processed_ATL06_20181214194017_11790102_001_01.h5',\n",
       " '/home/jovyan/data/Outputs_noCoverage/processed_ATL06_20190111063212_02110206_001_01.h5',\n",
       " '/home/jovyan/data/Outputs_noCoverage/processed_ATL06_20190112181620_02340202_001_01.h5',\n",
       " '/home/jovyan/data/Outputs_noCoverage/processed_ATL06_20190209050825_06530206_001_01.h5',\n",
       " '/home/jovyan/data/Outputs_noCoverage/processed_ATL06_20190214164413_07370202_001_01.h5']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fns = []\n",
    "\n",
    "# Get the filenames and append to a list\n",
    "for f in glob.glob(data_dir + \"*.h5\"):\n",
    "    # append full filename to list of filenames\n",
    "    fns.append(f)  \n",
    "\n",
    "# sort the list\n",
    "fns.sort()\n",
    "fns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/jovyan/data/Outputs_noCoverage/processed_ATL06_20181110092841_06530106_001_01.h5'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify the last HDF5 file from the list as a test case\n",
    "myfile_fn = fns[0]\n",
    "myfile_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From Ben Smith's Clouds and data quality tutorial - read datasets from ATL06\n",
    "\n",
    "def ATL06_to_dict(filename, dataset_dict):\n",
    "    \"\"\"\n",
    "        Read selected datasets from an ATL06 file\n",
    "\n",
    "        Input arguments:\n",
    "            filename: ATl06 file to read\n",
    "            dataset_dict: A dictinary describing the fields to be read\n",
    "                    keys give the group names to be read, \n",
    "                    entries are lists of datasets within the groups\n",
    "        Output argument:\n",
    "            D6: dictionary containing ATL06 data.  Each dataset in \n",
    "                dataset_dict has its own entry in D6.  Each dataset \n",
    "                in D6 contains a list of numpy arrays containing the \n",
    "                data\n",
    "    \"\"\"\n",
    "    \n",
    "    D6=[]\n",
    "    pairs=[1, 2, 3]\n",
    "    beams=['l','r']\n",
    "    # open the HDF5 file\n",
    "    with h5py.File(filename) as h5f:\n",
    "        # loop over beam pairs\n",
    "        for pair in pairs:\n",
    "            # loop over beams\n",
    "            for beam_ind, beam in enumerate(beams):\n",
    "                # check if a beam exists, if not, skip it\n",
    "                if '/gt%d%s/land_ice_segments' % (pair, beam) not in h5f:\n",
    "                    continue\n",
    "                # loop over the groups in the dataset dictionary\n",
    "                temp={}\n",
    "                for group in dataset_dict.keys():\n",
    "                    for dataset in dataset_dict[group]:\n",
    "                        DS='/gt%d%s/%s/%s' % (pair, beam, group, dataset)\n",
    "                        # since a dataset may not exist in a file, we're going to try to read it, and if it doesn't work, we'll move on to the next:\n",
    "                        try:\n",
    "                            temp[dataset]=np.array(h5f[DS])\n",
    "                            # some parameters have a _FillValue attribute.  If it exists, use it to identify bad values, and set them to np.NaN\n",
    "                            if '_FillValue' in h5f[DS].attrs:\n",
    "                                fill_value=h5f[DS].attrs['_FillValue']\n",
    "                                bad=temp[dataset]==fill_value\n",
    "                                temp[dataset]=np.float64(temp[dataset])\n",
    "                                temp[dataset][bad]=np.NaN\n",
    "                        except KeyError as e:\n",
    "                            pass\n",
    "                if len(temp) > 0:\n",
    "                    # it's sometimes convenient to have the beam and the pair as part of the output data structure: This is how we put them there.\n",
    "                    temp['pair']=np.zeros_like(temp['h_li'])+pair\n",
    "                    temp['beam']=np.zeros_like(temp['h_li'])+beam_ind\n",
    "                    temp['filename']=filename\n",
    "                    D6.append(temp)\n",
    "    return D6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dict={'land_ice_segments':['h_li', 'delta_time','longitude','latitude'], 'land_ice_segments/ground_track':['x_atc']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "i=0\n",
    "# read ATL06 into a dictionary\n",
    "ATL06_file=myfile_fn\n",
    "D6_list=ATL06_to_dict(ATL06_file, dataset_dict)\n",
    "\n",
    "# pick out gt1l:\n",
    "D6 = D6_list[i]\n",
    "\n",
    "# plot ATL06:\n",
    "f1,ax = plt.subplots(num=1)\n",
    "ax.plot(D6['x_atc'], D6['h_li'],'b.', markersize=4, label='ATL06')\n",
    "lgd = ax.legend(loc=3,frameon=False)\n",
    "ax.set_xlabel('h_li, m')\n",
    "ax.set_ylabel('h, m')\n",
    "plt.title(fns[i][69:-25])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fraction of bad segments = 0.000\n"
     ]
    }
   ],
   "source": [
    "num_bad_segments=np.sum((D6['x_atc'] > 2.904e7) & (D6['x_atc'] < 2.912e7) & np.isfinite(D6['h_li']))\n",
    "num_possible_segments=(2.912e7 - 2.904e7)/20\n",
    "F_bad=num_bad_segments/num_possible_segments\n",
    "print(\"fraction of bad segments = %3.3f\" % F_bad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot of all 14 granules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 44 beam/pair combinations\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fb77ebbe5f8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/lib/python3.6/site-packages/matplotlib/colors.py:885: UserWarning: Warning: converting a masked element to nan.\n",
      "  dtype = np.min_scalar_type(value)\n",
      "/srv/conda/lib/python3.6/site-packages/numpy/ma/core.py:713: UserWarning: Warning: converting a masked element to nan.\n",
      "  data = np.array(a, copy=False, subok=subok)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "D6=[]\n",
    "pairs=[1, 2, 3]\n",
    "beams=['l','r']\n",
    "\n",
    "files=glob.glob(data_dir+'/*.h5')\n",
    "for file in files:\n",
    "    this_name=os.path.basename(file)\n",
    "    D6 += ATL06_to_dict(file, dataset_dict)\n",
    "print(\"read %d beam/pair combinations\" % (len(D6)))\n",
    "\n",
    "# now plot the results:\n",
    "plt.figure();\n",
    "for Di in D6:\n",
    "    plt.scatter(Di['longitude'], Di['latitude'], c=Di['h_li'], \n",
    "                #vmin=0, vmax=2000, \n",
    "                linewidth=0)\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('latitude')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GM WV files that have the best overlapping ICESat-2 tracks\n",
    "(stereo-pair and MS and PAN, no SWIR, will have to eventually classify using NRGB_V)\n",
    "\n",
    "18APR03181123-M1BS-502016740020_01_P001-BROWSE\n",
    "18APR03181123-M1BS-502016740010_02_P001-BROWSE\n",
    "\n",
    "Overlapping track dates\n",
    "11.15\n",
    "12.13\n",
    "\n",
    "\n",
    "Current tasks:\n",
    "**Use pan bands and try to get a DEM**\n",
    "\n",
    "Later tasks:\n",
    "1. scp imagery over to tahoma (ntf, xml)\n",
    "2. run through to toa reflectance following workflows\n",
    "3. classify with and without rocks class using the trained models from test case\n",
    "4. upload the classifications to the drive\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
